Machine Learning Engineer

Role Overview

Machine Learning Engineer to design, build, evaluate, and deploy advanced ML systems for AI safety, security, and intelligence use cases. Own work across the full ML lifecycle and collaborate with interdisciplinary teams to deliver production-ready solutions.

Responsibilities

• Design, train, evaluate, and deploy ML models for text, image, audio, and multimodal data.
• Build classification systems for safety, security, abuse detection, and intelligence.
• Run benchmarking and evaluation experiments for LLMs and multimodal models.
• Apply model distillation, optimization, and fine-tuning to improve performance and deployability.
• Design evaluation pipelines, metrics, and testing frameworks for model capabilities and safety.
• Build agentic systems and automated workflows for evaluation, red teaming, research, and experimentation.
• Manage ML projects from research/prototype through production deployment and monitoring.
• Collaborate with software engineers to productionize ML systems and continuously improve them.
• Provide technical expertise across client projects and internal research initiatives.

Requirements

• 3 to 5+ years of experience building and deploying ML systems.
• Proficiency in Python and ML frameworks such as PyTorch and/or TensorFlow.
• Experience with multiple modalities, with expertise in computer vision and/or NLP.
• Experience training, fine-tuning, evaluating, and deploying models in production.
• Ability to design evaluation methodologies, benchmarking systems, and performance metrics.
• Familiarity with MLOps tools/practices: Docker, Kubernetes, CI/CD, MLflow.
• Experience with cloud platforms such as GCP (preferred), AWS, or Azure.

Nice to Have

• Experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research.
• Experience with frontier language models, cybersecurity, trust & safety, or retrieval-augmented generation (RAG) frameworks.